Parametric study of a triboelectric transducer in total knee replacement application
Bibliographic record
Abstract
Triboelectric energy harvesting is a relatively new technology showing promise for biomedical applications. This study investigates a triboelectric energy transducer for potential applications in total knee replacement both as an energy harvester and a sensor. The sensor can be used to monitor loads at the knee joint. The proposed transducer generates an electrical signal that is directly related to the periodic mechanical load from walking. The proportionality between the generated electrical signal and the load transferred to the knee enables triboelectric transducers to be used as self-powered active load sensors. We analyzed the performance of a triboelectric transducer when subjected to simulated gait loading on a joint motion simulator. Two different designs were evaluated: one made of Titanium on Aluminum (Ti-PDMS-Al) and the other made of Titanium on Titanium (Ti-PDMS-Ti). The Ti-PDMS-Ti design generates more power than Ti-PDMS-Al and was used to optimize the structural parameters. Our analysis found these optimal parameters for the Ti-PDMS-Ti design: external resistance of 304 MΩ, a gap of 550 µm, and a thickness of the triboelectric layer of 50 µm. Those parameters were optimized by varying resistance, gap, and the thickness while measuring the power outputs. Using the optimized parameters, the transducer was tested under different axial loads to check the viability of the harvester to act as a self-powered load sensor to estimate the knee loads. The forces transmitted across the knee joint during activities of daily living can be directly measured and used for self-powering, which can lead to improving the total knee implant functions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".